Archive for Player Analysis

Who got the most out of the Shelby Miller trade?

At the moment, based on present values of prospects, players and wins? It’s the Braves. Based on the industry consensus? It’s the Braves. Based on which team will be better next season? Maybe still the Braves. How did Arizona get so comprehensively fleeced in this deal? There have been some great articles written on FanGraphs these past two weeks about the GM of the Arizona Diamondbacks. Dave Stewart seems to want to do things differently from all other teams, going with his gut when building a roster. Gut decisions can work. His gut identified Shelby Miller as a good player and so wanted him on his team. But wow did this gut decision miss something. How much something? How about 225 million dollars?

That’s right, I am suggesting that, with present values of prospects, players and wins, Dave Stewart, GM, gave away $225M more value in this trade than he got back. See below for how I got to that number.

FanGraphs’ staff have been using a great contract tool recently to estimate the value of major-league free agents. It estimates that players get better each year until aged 27 then worse each year after reaching 31. I’ll be following their method, which estimates an aging curve using WAR where players improve by 0.25 WAR each year in their 18-27 age years, keep steady WAR in their 28-30 age years and get worse by 0.5 WAR each year in their 31-37 age years. It also includes an inflation increase in the market value for each WAR, starting at $8.0M in 2016 and increasing by 5% each year going forwards.  We end up with an estimated value for players over the lifespan of their contract.

I’m going to use last season’s FanGraphs WAR as the starting value for any major-league players (I’ll discuss the minor-league prospects later). I realise I could use ZiPS or many other predictors of players performance (which offer much lower WAR values), but it seems fair as both major-league players in the deal have some issues with their peripheral numbers that seem to balance out. The tool is an estimator based on current performance, so it seems fair we start with what they achieved this last year. Anyway, let’s get started.

The Major Leaguers

Shelby Miller (worth $98.48 M) – to Arizona

Ender Inciarte (worth $172.91 M) – to Atlanta

Holy cow! Not looking good for Arizona already. Shocked how much Inciarte is worth by this model? Me too. Those wins get expensive. He’ll do well to keep his performance at this level, but I’d argue that Miller has the same issue.

The Minor Leaguers

This isn’t quite as simple to work out. Minor-league players have a habit of not making it to the majors (an average 70% bust rate of ranked players not making a significant contribution to the major league team, according to this excellent article by Scott McKinney). I have used the valuations on ranked MLB prospects from Kevin Creagh and Steve DiMiceli with a couple of modifications.

Summarised, using historical prospect rankings, they took prospects ranked between 1-100 in the top prospect rankings each year and found the average WAR produced over their first six major league seasons at different rankings. These values are in the table below:

Creagh and DiMiceli were looking at surplus value produced by a prospect. I’m more interested in total value. For each ranked prospect (Blair and Swanson) I found the average WAR produced by historical players with a similar prospect ranking (potentially flawed; I’d probably prefer median WAR for each group), then used an inflation model (again 5% per year starting at $8.0M/WAR in 2016) and an assumption that 2/3 of a prospect’s value is accrued during years 4-6 in the majors to find an estimate for total value.

Finally, some minor-league prospects don’t improve enough to reach the majors (they “bust”) so they aren’t as valuable as major-league players. This reduces the value of the prospect. Lower-ranked players are more likely to bust than higher ones so they should have a greater reduction in their value. The historical likelihoods of a prospect bust are shown in the table above (Bust % Zero WAR or less) for different ranks of prospect (again from Creagh and DiMiceli).  To account for the chance of a prospect bust I reduce their value by a factor of the bust percentage, taken from the table above (% Zero WAR or less). It isn’t perfect, but seems reasonable, I’m happy to discuss in the comments.

Dansby Swanson (worth $137.2M) – to Atlanta

• #10 prospect currently (on MLB.com)
• Hitter
• Worth 15.6 WAR on average in years 0-6
• Arrives in majors in 2017
• 33% of value in years 0-3 (at 2018 cost per WAR) – 5.2 WAR – $45.76M
• 67% of value in years 4-6 (at 2021 cost per WAR) – 10.4 WAR – $105.04M
• Bust chance of #10 hitting prospect – 9%
• Estimated total value – $137.2M

Aaron Blair (worth $14.85M) – to Atlanta

• #61 prospect currently (on MLB.com)
• Pitcher
• Worth 3.4 WAR on average in years 0-6
• Arrives in majors in 2016
• Assume only lasts 3 years in majors (due to low WAR total)
• 100% of value in years 0-3 (at 2017 cost per WAR) – 3.4 WAR – $28.56M
• Bust chance of #61 pitching prospect – 48%
• Estimated total value – $14.85M

Gabe Speier (worth $negligible) – sorry Gabe – this trade wasn’t about you.

Final value totals

Arizona:

• Shelby Miller – $98.5M

Atlanta:

• Ender Inciarte – $172.9M
• Aaron Blair – $14.9M
• Dansby Swanson – $137.2M
• TOTAL – $325M

DIFFERENCE IN VALUE – $226.5M

There are a number of obvious caveats here. Miller could be better than this, Inciarte may not be that good, Swanson may never make it, Blair may never make it. However, at this moment, these are some of the values that you could reasonably ascribe to these assets. This is a staggering loss for Arizona. In what business can you lose $225M dollars in one transaction and keep your job? By this rather flawed measure, the Braves have just increased the value of their organisation by $225M. That pays for half a new stadium. Or Jason Heyward’s recent contract. Or the next 3 years of performance of Mike Trout (Trout is seriously valuable). I realise that the money can’t be accessed like that, but still, wow. Dave Stewart might be using his gut feeling when making deals, but he’d better start listening more to his analytics department or he’s liable to get robbed again.

A lot of the inspiration (it wasn’t plagiarism, honest) for this article came from Craig Edwards and his piece on “Attempting to rationalise the Shelby Miller Trade”. I just took it a different way. Thanks to Craig though! You should read it – http://www.fangraphs.com/blogs/attempting-to-rationalize-the-shelby-miller-trade/

By now I think everyone is aware of Thing 1 and Thing 2, the splitter/change-up pitch currently used by Tampa Bay Rays starters Alex Cobb and Jake Odorizzi. (If you are unaware of them you can read up on it here.) An oblique injury limited Odorizzi to 169.1 innings over 28 starts in 2015, but the signs of a potential big-time pitcher were present throughout most of the campaign.

I took Odorizzi’s 2015 season with Thing 2 now in his arsenal and compared it to seasons that some of the top starters in the league have had early on in their careers. Here is what I found:

Jake Odorizzi 2015 (age 25) K% 21.4  BB% 6.6  Hard hit rate 26.7%  Soft hit rate 19.4%

Johnny Cueto 2010 (age 24) K% 17.7  BB% 7.2  Hard hit rate 30.2%  Soft hit rate 19.6%

Zack Greinke 2008 (age 24) K% 21.5  BB% 6.6  Hard hit rate 27.8%  Soft hit rate 19.6%

Jake Arrieta 2012 (age 26) K% 22.0 BB% 7.1 Hard hit rate 25.6%  Soft hit rate 20.5%

Max Scherzer 2010 (age 26) K% 23.0  BB% 8.8%  Hard hit rate 29.6%  Soft hit rate 19.1%

Now I know the rest of these pitchers didn’t add their own version of a Dr. Seuss-named pitch, or any pitch for that matter. However all of these pitchers continued to make a leap in their following season. Cueto’s 2011 saw his contact rates make almost a complete flip (27.7% soft 21.3 hard). Greinke took a huge leap winning the AL Cy Young in 2009. Arrieta’s 2013 saw him get traded to the Cubs and begin his trend upwards leading to the NL Cy Young in 2015. Max Scherzer had his walk percentage drop to 6.7% and his hard and soft hit rates move to almost even with each other(23.5% and 22% respectively).

You can see that Odorizzi’s K% and BB% are very similar to all four pitchers (especially Greinke’s), and also his hard and soft hit percentages are right in the middle of this data sample. Odorizzi was able to accomplish these batted ball rates, despite having the lowest groundball percentage of the five. Odorizzi’s ground ball percentage was 37.3% last season. Cueto, Greinke, Arrieta and Scherzer all had a groundball percentage higher than 40% with Scherzer being the lowest at 40.3%.

If Thing 2’s development stunts or the command issues that have plagued Odorizzi at different times in both the majors and minors crop up, then Odorizzi’s 2015 ratios (as listed above) line up extremely close to Aaron Harang’s career ratios — and that’s not a world that I’m ready to live in. I’m betting on the fact Odorizzi still has age and development time on his side, as he won’t turn 25 until right before the 2016 season starts. Along with Tampa’s relatively positive history of developing starting pitching and their use of defensive shifts and alignments, there are plenty of reasons to get excited about Odorizzi’s short term and long term future.

Brian. Cashman.

Probably one of the more unpopular figures in New York sports (for no good reason) has struck again and made a great deal for the Yankees.

I’m a Cashman defender, through and through. I think that 15 playoff appearances in 18 seasons, 6 pennants, and 4 championships is a stellar résumé, and that he doesn’t get enough credit for it, regardless of how big the payroll is, or how much control he really had early on. We’ve seen Cashman at his best these past few seasons. Sure, they haven’t been the most successful for the Yankees; anything less than a championship is a failure. That’s the Steinbrenner way, and the way it should be. But we’ve seen that Cashman can operate without an infinite payroll and can make moves other than opening the Yankee’s checkbook.

You can criticize Cashman for some free agent signings, sure. You can say he didn’t have to do much to win his first championships and inherited a great roster; that’s more than fair. You can’t, however, deny that Cashman is a master of trades, especially recently. He understands trading from a surplus as well as anyone. And he does so to acquire either low-risk, win-now pieces, or young, talented, potential-filled, cost controlled pieces.

And with this latest trade of Adam Warren and Brendan Ryan for Starlin Castro, Cashmoney has done it again!

Before we dive into this latest case of Cashman genius, let’s highlight some of his recent gems.

Catcher JR Murphy to the Twins for outfielder Aaron Hicks, November 2015- I like Murphy a lot. He seemed like a great guy, and was a good player, but the Yankees were loaded with catchers. McCann is locked in for 3 more seasons and is basically immovable with his contract. Gary Sanchez has been destroying the Arizona Fall League and seems like a legit power bat. Austin Romine is somewhat boring, but has the skills to be a serviceable backup catcher, and many are still high on Luis Torrens. He missed last season with an injury, but is still viewed as a solid prospect. Murphy was merely a commodity. Hicks, once a top prospect, is a switch-hitting 26 year old outfielder, and seemed to turn the corner last season, posting the highest average, slugging percentage, wOBA, and WAR of his career. He hits lefties very well too, batting .307 against southpaws last season. At worst, Hicks is an above average fourth outfielder who will play very solid defense, and provide speed and right handed pinch-hitting ability off the bench. At best, he has a similar career turnaround to Carlos Gomez (as Paul Sporer notes), and becomes the All Star outfielder he was once expected to be. He’s under team control through 2019.

Infielder Martin Prado and RHP David Phelps to the Marlins for RHP Nathan Eovaldi and 1B/OF Garrett Jones, December 2014- Garrett Jones was obviously a bust but this was still a great trade for the Yankees. A lot of the Yankees trades have worked out, but a good way to look at trades (or life in general if we really want to get deep here) is to not be so results-oriented. Use your resources to make the best decision possible, and let the pieces fall where they may. The Yankees traded a fringe starter/long reliever and aging utility man for a power lefty off the bench and a promising, young, flame-throwing arm. And it worked out really well. David Phelps is David Phelps, and Martin Prado wouldn’t have won the Yankees the pennant last season. Nathan Eovaldi looked like a changed man under Larry Rothschild’s guidance last season. Adding a nasty split to go with his high-90’s fastball has done wonders for him. He’s able to change hitters’ eye-levels more effectively, meaning more strikeouts and less hard contact. It paid off last season. He won 14 games and got better as the season progressed, striking out 8 batters per 9 innings, and posting a 3.67 ERA in the second half. Eovaldi looks like he’s on his way to being a solid 2-3 starter, and is under team control through 2017.

RHP Shane Greene to the Tigers, received shortstop Didi Gregorius from the Diamondbacks, December 2014– This may go down as one of the better trades the Yankees have ever made. Big statement, I know. But the Yankees traded a barely major league starter for what looks like the shortstop of the future. At the time, Greene was viewed as a back of the rotation starter at best. Fast forward a year and he posted an ERA of nearly 7, and may not have a job next season. Gregorius, meanwhile, overcame a slow start and turned into one of the Yankees most valuable assets. He hit .294 with a .762 OPS in the second half, ranked as the 4th best shortstop in baseball(!) per fWAR, and played stellar defense. Jorge Matteo is on the horizon, but Gregorius is the shortstop of the present and the future if he keeps this up. I envision a breakout season coming for the Dutchman. He’s under team control through 2019.

All 3 of those trades were low-risk deals, dealing from a surplus for high-upside guys under team control for the foreseeable future. Two have worked out very well, and the third has a great chance to.

(Side note: When I was looking through spotrac.com for these exact contracts, and was reminded that Jacoby Ellsbury is under contract through 2021, I almost threw up.)

Cashman, however, has also showed he can make great trades in season for win-now players. In 2014, he gave up Vidal Nuno for Brandon McCarthy. They lost McCarthy that offseason, but Nuno is no Clayton Kershaw and McCarthy was great for them down the stretch, performing at an ace-like level; he had a sub 3.00 ERA. The deal he made for Chase Headley that season was similar. While fans may be understandably upset at it now, as Solarte had a solid season last year, and Headley, for the most part, did not, it was a really good trade at the time and still could be for the future. Solarte had just a few months of MLB experience and while he started off hot, he was drastically slowing down. Headley had a 31 home run season, All Star appearance, and Gold Glove under his belt. The Yankees locked Headley up this past offseason through 2018, and will want him to improve. Solarte actually had a higher average, wOBA, wRC+, and WAR last season. Headley, though, has proven he can be an All Star player, and he should rebound this season, especially defensively. And the point is, at the time, it was a really smart move. The Yankees also included Rafael De Paula in that deal, but has done nothing of note and is still floating around the minors.

I also feel obligated to mention the Kelly Johnson for Stephen Drew trade in 2014 with the Red Sox. Probably the most hilarious trade I’ve ever seen for so, so many reasons, but I digress.

Back to the deal at hand. Warren and Ryan for Castro. A fringe starter/middle reliever and a veteran, light-hitting (to be generous), backup middle infielder for a 25 year old, 3-time All Star, once top prospect, yet still very promising middle infielder. I know that sounds too simple, but that really is what this deal is.

Brendan Ryan… let’s just get this out of the way early. This is addition by subtraction for the Yankees if we’re being honest. He’s a .234 lifetime hitter with 19 home runs in 2,872 plate appearances. I wish I was making that up, but I’m not. And for a guy who supposedly has a great glove, I saw him make/not make a number of questionable plays last season. He was wasting a roster spot. Now his role can go to someone more promising like Dustin Ackley or Rob Refsnyder.

Parting ways with Adam Warren isn’t easy, but it’s not the end of the world. Warren is a good pitcher. I liked watching him grow these past few seasons and wish the Yankees could have kept him in the rotation last season, where he was very reliable, although he did seem to also find a niche as middle/late inning reliever. Warren “knows how to pitch” to use a cliche. He doesn’t blow you away, but he works his fastball in with his offspeed stuff well, locates his pitches, and gets people out. Pitchers usually improve when they move to the NL, and with this opportunity to finally be a starter, I expect Warren to be a nice addition for the Cubs next season. He should be a solid back of the rotation starter, or reliable reliever if they go that route. Not sure Adam Warren is the key to ending their century long World Series drought, but he doesn’t hurt them, that’s for sure.

The idea that the Yankees are giving up some Cy Young caliber starter, however, is absurd. Like I said, Warren is a 4-5 starter and the Yankees just don’t have room for him. Tanaka, Severino, Eovaldi, and Pineda are all rightfully ahead of him. And Sabathia, Nova, and Mitchell are in the mix as well. Plus, it’s almost a certainty that the Yankees will add another starter this offseason, so there really was just no room for him in the rotation. He would’ve been a nice arm in the bullpen, as the Yankees need another right-hander out there so Dellin Betances can rest more, but he can be replaced. There’s always plenty of right-handed relievers available on the free-agent market, and the Yankees have some in-house candidates as well. This does, however, make trading Miller even dumber, but that hopefully shouldn’t be happening anyway. The bottom line is, while Warren is a nice pitcher, he was no more than a middle reliever/depth starter for the Yankees. Turning him into Starlin Castro is gold by Cashman.

Speaking of Castro, let’s get into the real headliner of the trade. Castro will be just 26 at the start of the 2016 season. He’s been an All Star, he’s got a proven bat, and while he’s had some troubles, he seems to be trending in the right direction. He’s got a career slash of .281/.321/404. His wOBA is .316 and his wRC+ is a below-average 96. Every full season he’s played, he’s hit between 10 and 14 home runs. Now, none of these numbers set the world on fire, but there’s been very promising stretches sandwiched in there, and when you consider he’s a middle infielder, these numbers look much better.

The problem with Castro has been inconsistencies. Last season he was not very good offensively, and he wasn’t in 2013 either. But he had very promising seasons the rest of his career; 2010, 2011, 2012, and 2014. Over those 4 years, his wRC+ was 99 or higher each season, he’s hit at least .283 in all of them (over .300 twice), and has averaged a WAR of 2.9 per 162 games. Those are really good numbers, especially for a middle infielder. We can’t completely discount his two poor seasons, but a change of his scenery may be all he needs, as the talent is clearly there.

Along the lines of a change of scenery is his position change from short to second in August of last season. In his 33 games at second base last year, he hit 5 home runs, drove in in 22 runs, and had a slash of .339/.358/.583. Small sample size, yes, and logically there should be no relationship between what position a guy’s playing and his ability to hit, but maybe that’s the case for Castro. If he feels more comfortable at second, it could be enough to get his mind right and allow his talents to take over. He’s still before his prime, and he’s under team control through 2020 for an affordable salary. It’s a risk the Yankees needed to and can take. Stephen Drew just had the worst year and half I’ve ever seen, and while Ackley and Refsnyder have potential, they’re not as good as Castro. Ackley can now have a utility role, while Refsnyder can perhaps be used as part of a package deal to get a frontline starter.

On a larger scale, this trade shows the genius of Cashman’s trading ability, and the Yankees’ continued win-while-rebuild mode. They’re keeping their top prospects, yet still are getting younger and more athletic by trading from areas of surplus and buying low and selling high on players. For all the talk of the Yankees being an old, veteran team, they very quietly are assembling a great, young core. Gregorius, Castro, Greg Bird, Aaron Judge, Matteo, Refsnyder, Hicks, Severino, Eovaldi, Pineda, Tanaka, and Betances are all either top prospects, proven players, potential budding stars or somewhere in between. Not one of them is older than 27 and all are under team control for the foreseeable future. With this young core, and a ton of money coming off the books very soon, the future is bright in the Bronx. You can thank Brian Cashman for that.

This article was co-authored by Daanish Mulla – @DanMMulla

Last month, we wrote an article on calculating a pitcher’s “stuff”. We were quite pleased with how our equation performed with respect to predicting a pitcher’s strikeout rate and his xFIP. Part of the discussion surrounding the equation was what exactly is stuff? Well, in our case, stuff can be thought of as a three-dimensional shape, where the three axes of the shape represent a pitcher’s peak velocity, a pitcher’s change in velocity between their fastest and slowest pitch, and the amount of distance that their pitches can break. In other words, it aims to represent the range in pitch velocity and movement batters must account for during any given at-bat against a particular pitcher.

However, there was still some room for improvement, and with help from the FanGraphs community, we’ve slightly modified our equation to improve various performance predictions. The first major change came from comparing faster breaking balls versus slower breaking pitches with greater movement. In our original stuff metric, pitchers with a slow, looping breaking ball received more benefit than pitchers throwing a fast breaking ball. I queried the PitchF/x database to see how swinging strike rates and batting average changed against curveballs with respect to pitch speed during the 2014 season. Pitches that were thrown for at least 1% of all pitches were included in this analysis. As you can see in the figure, swinging-strike percentage increases exponentially after 75mph, and is nearly 15% higher at 85mph than at 75mph. This encouraged us to find a better way to account for faster breaking balls.

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Secondly, the original metric did not account for pitch frequency. The Pitch Arsenal metric was improved from it’s original state by accounting for this, and realistically – a pitcher should be given more credit for a great pitch that they throw frequently, as opposed to a great pitch that they rarely throw. To account for this, pitches were classified as either off-speed/breaking or fastballs. The sum of pitch uses for each of these classifications was then used to modify the values in the equation. With that in mind, here’s how we have proposed to modify the stuff equation.

For a pitch to be included in the analysis, it had to be thrown by the pitcher 100 times. Just like the original stuff equation, z-scores were determined for the fastest pitch the pitcher threw, and for the amount of movement that could be seen with respect to that fastball, from the remaining pitches.  For further analysis, only qualified starters were used (those who threw 162 innings in the 2015 season).

Furthermore, z-scores were also determined for the % change in speed between the pitcher’s fastest and slowest pitch. Another z-score was determined for the velocity of the fastest pitch, between curveball, slider, or knuckle-curve. Frequencies were determined for the proportion of fastballs thrown by a pitcher, and the remaining non-fastball pitches. The z-score for velocity was multiplied by the fastball percentage, and the remaining z-scores were multiplied by the non-fastball frequency. The z-scores for peak velocity of breaking pitch and change in velocity were used to determine “pitch strategy” – either, power breaking ball, or change in speed. Whichever z-score was greater, was used in the final stuff equation.

So, the final “stuff” equation is as follows:

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To begin validation of the equation, the stuff value was then correlated with K/9 for all qualifying starters. This resulted in a predicted R value of 0.53 (figure 2), compared to the value of 0.42 from the original stuff equation.

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We’ve since applied the stuff equation to all pitchers from 2007 to 2015 to try and get an idea of the range of the metric. Here’s what we found. For interpretation of this figure, if a pitcher has a stuff value of 0.90, his stuff is better than 75% of all pitchers since 2007. If the value is 2.0, they have stuff that is better than approximately 99% of all pitchers since 2007. To put that in perspective, that means their stuff is better than nearly 4000 other starting pitchers. You’ll notice that in our list of the top 30 pitchers from 2015 – all of these pitchers fall within the top 15% range of stuff. These are elite pitchers with respect to this metric.

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These data have a wealth of applications, such as how a pitcher returns from injury or has even changed his repertoire between years. For example, the jump Chris Bassitt made from 2014 to 2015 – going from someone in the bottom half of the metric to the 99^th %ile. Similar to the Arsenal score, there is an application of these data in determining a pitcher on the verge of a breakout (perhaps the Joe Kelly of the second half of 2015 is the real Joe Kelly).

However, we felt that it would be in our best interest to let the community decide just how useful the metric was, so we’re making our evaluation data from 2007 to 2015 available in the form of a Google sheet. Simply select the pitcher you’d like to evaluate, and their stuff scores and xFIPs will be graphed for you. We’ve also posted the entirety of stuff scores from the 2015 season.

2015 Season

https://docs.google.com/spreadsheets/d/1picxCRD1OWpaeDq2H8uxC7jyR6fH7fpj5gOZjQGWsu4/pubhtml

Stuff worksheet

https://docs.google.com/spreadsheets/d/1PU3u3sJpr_jv70VAJIlyXnvOh4pq56l7eXuo70Py81Y/edit?usp=sharing

Philosophically, we feel that the stuff metric has a great benefit for advanced scouting, because it relies on measures that are solely dependent on the pitcher, and not an interaction of the pitcher and the hitter. Thanks to the FanGraphs community, r/baseball, and Eno Sarris for all of the support with this project.

Yes. Probably. If you’re in a hurry, you can now go do whatever you should be doing instead of reading about no-longer-prospecty baseball players. But if you’re not in a hurry, know this: there’s a chance he can survive, if only a small chance.

Kyle Drabek is rowing against a mighty tide that seeks to dash what’s left of his career against the jagged rocks. The former Phillies first-round pick (don’t fret, Ruben-haters — the really good guys in this draft were already gone) had a mediocre minor league career followed by a wretched major league one. Over 177 injury-plagued innings, Drabek has been a TTO arsonist: 6.1 K/9, 5.7 BB/9, and 1.2 HR/9. His career ERA/FIP is 5.27/5.42. When hitters die and go to heaven, they face him every night. And yet the Arizona Diamondbacks recently signed him to a minor league deal. What might they be thinking?

Drabek has been through Tommy John surgery twice, in 2007 and again in 2012. Here’s a list of repeat TJ offenders — you can sort on “Back to playing” to see who, well, made it back to playing. It’s largely a grim list, but there are some pitchers who came back to perform decently. As you can see from perusing the list, and this post, the vast majority of those are relievers.

Drabek’s major league performance so far suggests little more should be expected of him. In the expansion era there have been 73 starting pitchers who “achieved” a FIP of 5+ in at least 150 innings before age 28. Here they are — Drabek checks in at #27.  The name that jumps out most prominently on this list is Joe Nathan:

Through age 27         IP        ERA        FIP         K/9          BB/9          HR/9

Nathan                       187       4.61       5.72          5.6            5.2               1.4

Drabek                       177       5.27        5.42         6.1             5.7               1.2

Entering his age-28 season, Nathan was a failed starter with one shoulder arthroscope to his credit. That year the Giants converted him into reliever, and after season’s end converted him into A.J. Pierzynski. Nathan went on to rack up the eighth-most saves in MLB history, which is a pretty fair achievement even if you aren’t into saves.

How did he do it? Unfortunately, pitch-by-pitch data isn’t available back to the years (1999-2000) when Nathan did most of his damage as a starter. But a look at Nathan’s pitch values nevertheless suggests some clues. One big clue in particular: his slider was devastating. By fleeing to the bullpen, Nathan probably was able to add a little heat to the slider, and perhaps able to throw it more often. Hitters would only see him once as a reliever, but this may not have been a huge factor, since hitters were beating Nathan like a drum as soon as the cute guest-PA-announcer-kid finished shouting “play ball!” Get to him a third time, though, and it was like walking to the plate with a plutonium-corked bat.

One can imagine that being in the bullpen enabled Nathan to add some velo and subtract the amount of times he threw his weaker pitches. Salomon Torres presents a similar profile: a failed starter in the 1990s, Torres disappeared off the baseball earth for a few years before resurfacing with the Pirates as an effective reliever in 2002. Torres’ out pitches were the slider and splitter; he featured both as a reliever evolving toward the latter as he aged.

Justin Miller also made the transition from awful starter to solid reliever. Like the others, he did not wholly abandon a pitch when moving to the pen, but he placed greater emphasis on his — you guessed it — excellent slider, at the expense of his not-so-excellent fastball. Miller didn’t add much velocity in becoming a reliever; it seems to be the change in pitch selection that helped him turn the corner.

These career paths might be helpful signs for Drabek, but in at least two senses they aren’t: unlike the other guys mentioned above, Drabek lacks a carrying pitch. Nathan had an excellent slider, as did Miller and (at times) Torres, even when the rest of their pitches were failing them. Drabek’s pitches are all below average, so he appears to lack a safe base from which to make his bullpen transition (although to be fair, all of Drabek’s pitching stats suffer from the pain and embarrassment of small sample size). By the numbers, the curve is the least bad of his offerings; perhaps focusing on becoming a fastball-curve guy would benefit his development.

Focusing on the curve brings us to Drabek’s second problem: TJ survivors appear to struggle with breaking pitches. Throwing more curves at this stage of his career may be the last thing Drabek can (or is willing) to do. It’s not impossible: Jason Isringhausen leaned heavily on the curve as he remade himself into an elite closer. But there aren’t a lot of examples here.

Perhaps Drabek can develop his changeup, his second-least-bad pitch. It appears that, following his surgery, he tried to emphasize his cutter, a pitch that hasn’t been kind to him yet, but an approach that did help The Beard to become fearworthy. Perhaps a superior pitching coach could help Drabek, but here’s who the Snakes just hired.

Despite these difficulties, the Diamondbacks nevertheless have an incentive to pick through the Drabekian rubble to see if they can salvage any value. Even with their spiffy new TV deal the D’Backs will always be no better than second-tier in terms of resources and attendance, especially problematic with the Dodgers juggernaut in the same division. Finding cheap pitching hand-me-downs will enable the organization to invest elsewhere (as it is already doing with the lineup).

Given both Drabek’s limited major league success and his limited major league appearances, deciding how to reconstruct him may be even more difficult than such projects usually are. Drabek doesn’t have huge platoon splits, and while for now that means both southbats and northbats will feast on his pitches, over the longer haul it may mean that he could be useful as a swing man or multi-inning reliever.

The Diamondbacks have had success in re-imaging double-TJ survivor Daniel Hudson as a reliever, but he had already had much more success as a starter than any of the other pitchers mentioned in this post (except Tommy John himself). Indeed, the Snakes may move Hudson back to the rotation next year. But perhaps the work with Hudson has given the organization some clues for how to deal with a much more challenging project.

I’m rooting for Drabek, but I’m taking the under.

Projecting starting pitcher performance has proved more difficult than projecting hitter performance, mostly because pitcher skill level and performance tends to be more volatile. Another issue is that pitcher performance indicators are heavily reliant on batted-ball outcomes. This means a team’s defense and luck (e.g., softly hit balls that drop for hits) become a large part of run prevention, all of which are mostly out of the pitcher’s control. This realization has led to the development of a variety of pitching statistics that attempt to reduce pitcher performance into metrics that rely on outcomes only under pitcher control, such as walks, strikeouts, and home runs (e.g., Fielding Independent Pitching, FIP). Given that these metrics are the state of the art in terms of summarizing and describing a player’s past performance (not necessarily predictive measures; see Dave Cameron’s 2011 article here), it is useful to develop ways to attempt to predict these metrics from prior predictive statistics. As such, the goal of the current analysis was to develop prediction models using various regression tree methods that best predict starting pitcher performance metrics.

Data
Data for these analyses were compiled from several different sources, including Fangraphs.com and by using the ‘Lahman’ and ‘Retrosheet’ packages in R. Data were aggregated from the prior three seasons (2012-2014), as well as the 2015 regular season. The final data set included average performance statistics of starting pitchers from 2012-2014 who also pitched at least 50 innings during the 2015 season (N=127). The primary outcome was 2015 pitcher Wins Above Replacement (WAR). Predictors included aggregated values of over 30 performance metrics from the prior three seasons, including standard and advanced statistics (e.g., K-BB%), batted-ball measures (e.g., GB%), quality of contact statistics (e.g., hard contact %), and PITCHf/x measures (e.g., average fastball velocity).

Analytic Approach
The goal of this analysis was to use several different data modeling techniques to develop models that best predicted pitcher performance during the 2015 season from pitching data from the 2012-2014 seasons. Three separate techniques were utilized that fall within the general family of Classification and Regression Tree (CART) methods. CART methods use search procedure algorithms to find variables that are most important for prediction, then, determine the best possible cut point on the selected predictor in order to subset the data into multiple predictor spaces (Breiman, Friedman, Olshen, & Stone, 1984; Steinberg & Cola, 2009). These procedures allow for non-linear associations and higher order interactive effects. Regression trees were grown using several different packages in R, including the rpart and party packages. These packages are capable of growing large regression trees, but also include cost complexity and control parameters that allow for the assessment of over fit and tree size reduction. Next, a technique known as boosting using the gbm package in R was used to identify the predictors of highest importance for predicting pitcher performance. Although similar to ensemble CART methods that re-sample data to grow multiple large regression trees (e.g., bootstrap aggregation), boosting is a slow learning algorithm that grows regression trees sequentially, not independently. Each tree is fit to the residuals from the previous tree in order to isolate the misfit and re-shape the regression tree.

Results
First, the complete dataset was split in half in order to create training and test data sets. Next, the training data was used to fit a regression tree predicting 2015 WAR from all variables in the dataset. In the first model, liberal control parameters were set for the size of the tree, meaning a large tree was grown that selected all the best possible predictors. Each chosen predictor was then optimally split until each pitcher could be placed into a terminal node. The results from the initial model demonstrated that average strikeout rate per plate appearance (K%) was the best predictor of WAR with an optimal split of 22.39%. The initial model R2 demonstrated that 97% of the variance in WAR could be explained by this regression tree. Despite the high amount of variance explained, this model has likely over fit the data. In other words, the model is overly fit to the empirical data set, which means the model is too complex and unlikely to replicate across other samples. Reducing the size of the tree, or pruning the tree, will result in higher bias, but will reduce variance in the predicted values.

Initial Regression Tree Overfit to the Sample Data

In order to determine the optimal tree size (i.e., prune the tree) cost complexity pruning using 10-fold cross validation was done on the training data set. Based on the model deviance, the optimal tree size was determined to be between 4 and 6 terminal nodes. After pruning the tree, the R2 was reduced to .68, but the mean square error (MSE) was also reduced from 6.8 to 3.6 in the training data set. Next, the optimized tree was fit to the test data set, which produced an R2 of .57 and a MSE of 1.4. Surprisingly, after the initial split on K% the next-best predictors were related to quality of contact statistics (go here for more detailed information). Although there is a large amount of measurement error in these variables, it is still interesting these measures are predictive of WAR.

An inherent problem with regression trees is that continuous predictors with more unique values are more likely to be chosen because they contain a higher number of possible split points. The party package in R attempts to control for this issue by taking into account the distributional properties of the predictors (Hothorn, Hornik, & Zeileis, 2006). As such, similar models were fit predicting 2015 WAR using the party package in R. Results were similar to the model using the rpart package, which found that average strikeout rate was the best predictor with a split of 22.3%. However, it was determined that the data only required one optimal split, partitioning pitchers into those who were above and below a strikeout rate of 22.3% (see Figure below). Although this model explained significantly less variance in WAR (R2 =.29) than the larger tree, this model is likely to have higher stability and predictive utility in new samples.

Optimized Regression Tree using the Party Package

Finally, boosted regression trees were fit to the data to examine the optimal predictors of 2015 WAR. The number of trees (B=1,700) was chosen by examining the decline in the squared error loss for the out of the bag sample. The shrinkage parameter was set to λ =.001 with an interaction depth of d=1. For the training data the MSE was 1.79 and the R2 was .59. The model was then tested against the left-out half of the dataset (test dataset), which produced a MSE of 1.98 and an R2 of .55. Given the small differences in the R2 value and MSE for the test and training data sets, this model appears to show relative consistency. The most important predictors were determined by the importance function in the gbm package. Average strikeout rate, average fastball velocity, and average strikeouts per plate appearance minus walks per plate appearance were the most important predictors of 2015 WAR. To see a list of the relative influence of all variables refer to the table below.

Order of Variable Importance Predicting 2015 WAR

Based on these results it is clear that K% is a strong predictor of future WAR, which is not surprising because pitcher WAR is based on FIP (derived from K, BB, HR outcomes). Average fastball velocity and K% minus BB% also came out as a relatively strong predictors of WAR in the boosted regression tree models. Quality of contact was found to be an important predictor, but more analysis should be done in other samples to see if these measures have consistent predictive ability.

It was a defining moment in the 2015 World Series. Proven closer Jeurys Familia watches as a slow chopper is hit to third baseman David Wright. Wright glances back to Eric Hosmer on third and throws to first. Without hesitation, Hosmer sprints home and beats out a wild throw from first baseman Lucas Duda to stun the crowd at Citi Field and tie the game in the 9th. Hosmer made a big play on a big stage. But that wasn’t the only time in his career, the 2015 season, or even that World Series that Hosmer has shown a flair for the dramatic in big moments.

So I did a little digging.

The statistic “Clutch” quantifies how much better or worse a batter performs in high-leverage situations compared to a neutral situation. This does not necessarily make or break a good player. However, the statistic can show the track record of a player’s ability (or inability) to elevate his own game in big moments. The scale is centered at an average player clutch rating of 0 and typically ranges from -2 to 2 in any given season, with 2 being considered both a rare and excellent rating.  This statistic is better used to look at what has happened in the past rather than predict the future. To my surprise, Hosmer has dominated the clutch leader boards the last five years. Not Miggy, not Longo, not Big Papi. Hosmer.

Since his debut season in 2011, Hosmer has a cumulative clutch rating of 5.49 while the league average has been -0.38 according to fangraphs.com. The second-highest in that time span? Jacoby Ellsbury at 4.41, over a full point away. To this point in his career, Hosmer has a cumulative clutch rating that ranks 22nd in baseball history. He is ahead of legends such as Ken Griffey (5.35), Rickey Henderson (4.91), and fellow Royal George Brett (4.79). His 2015 campaign that yielded a clutch rating of 2.17 was one of the top 100 greatest clutch seasons ever recorded (tied for 63rd). Although there is no way to prove Hosmer will remain a clutch hitter, he is currently on pace to smash the all-time highest career clutch rating set by Hall-of-Famer Tony Gwynn (9.49).

So what makes Eric Hosmer clutch at the young age of 26? His high baseball IQ. He is constantly aware of the situation and knows what he has to do to produce runs for his team. Let’s use this past World Series as an example. In Game 1, Hosmer came to the plate in what was easily the one of the biggest at-bats of the season. Bottom of the 14th. Tie game. Bases loaded. Nobody out. Infield in. Seasoned veteran Bartolo Colon on the mound.

(Video copyright of MLB Advanced Media)

See what he did there? He knew the infield was in and he knew he could not do two things: strike out or hit the ball on the ground. He had to get the ball deep in the air, despite having a GB% of 52.2% in 2015. He gets a fastball in the heart of the zone and lifts the ball into right, deep enough to get the winning run home and record the seventh walk-off of his career.

The next day, he comes up in another big situation against young pitching phenom Jacob deGrom. Bottom of the 5th. Tie game. Runners on second and third.

(Video copyright of MLB Advanced Media)

With Escobar’s speed on second, Hosmer knew all he needed was a base hit to give the Royals a two-run lead. He sticks with a slider that caught a little too much of the bottom half of the plate and ropes a grounder up the middle. The 26-year-old didn’t let the moment get to him and over-swing, but instead took what the pitch gave to him. Clutch.

Although Royals fans have seen rather inconsistent numbers from Eric Hosmer through the first five years of his career, it is not overlooked how important he has been to bringing a winning atmosphere back to Kansas City as they had hoped he would. After all, he was the third overall pick in 2008 MLB amateur draft. But big expectations and big moments don’t scare Hosmer. He’s been the most clutch hitter in the league.

Moving on from an analysis of starting pitchers, we move to relievers.

Relief pitchers happen to be the easiest group of players to project as their final salary is nearly entirely driven by saves although for non-closers, holds become very important to differentiate between setup men (who make slightly more) and middle relievers.

For a RP who is arbitration-eligible for the first time, here are the statistics that correlate most with eventual salary:

Career SV: 83.28%

Platform SV: 79.07%

Career WPA: 38.15%

Career SV%: 35.60%

Career fWAR: 35.18%

Platform SV%: 27.06%

Platform SO: 25.75%

When initially looking for player comps, these are statistics we are going to focus on. Keep in mind that although ERA is not listed, it is nonetheless important as ERA is still one of the default statistics used during a hearing and one of the first bases for comparison.

Note: WPA and Shutdowns (SD) have strong correlations, however those two stats are not widespread enough to be used during a hearing. My model includes WPA, but does not include SD as the inclusion of SD de-emphasized the importance of saves while it inflated the salaries of situational relievers. While ideally that should be the way salaries are determined, that does not happen in practice so it made sense to omit SD from the model.

Let’s use Indians closer, Cody Allen, as an example of a first-year-eligible reliever. Cody Allen is arbitration-eligible for the first time going into 2016 with 3 years and 76 days of service time (3.076). In his platform season (2015), Allen recorded 34 saves with a 89.47 SV%, 99 SO and a 2.99 ERA. Over his career, Allen has compiled 60 saves with a 84.51 SV%, 4.19 WPA, 5.0 fWAR and a 2.64 ERA. The objective here is to find the players who avoided arbitration by signing a 1-year contract with statistics that are most similar to Allen’s. The more recent, the better. The best way to do that is to set a floor and a ceiling and then work your way towards the middle.

First, let’s look at David Aardsma’s 2009 platform season (old, but still useful). Like Allen, Aardsma was an effective closer with high save totals and a strong ERA. Aardsma recorded 38 saves, 80 SO with a 2.52 ERA. Over his career, Aardsma had compiled 38 saves with a 80.85 SV%, 2.25 WPA, 1.5 fWAR and a 4.38 ERA. Although the platform stats are very similar, Allen’s career numbers are far superior. Therefore, we can definitively state that Allen should receive more than Aardsma did. As such, Aardsma’s 2010 salary of $2.75 million should be the floor.

Next, let’s look at Greg Holland’s 2013 platform season. Like Allen, Holland was an effective closer with high save totals and a very strong ERA. Holland recorded 47 saves with a 94.0 SV%, 111 SO and a 1.21 ERA. Over his career, Holland had compiled 67 saves with a 88.16 SV%, 7.87 WPA, 6.9 fWAR and a 2.41 ERA. Although their career numbers are relatively close, Holland had a dominant platform season that surpassed Allen in every way. Therefore, we can definitively state Allen should receive less than Holland did. As such, Holland’s 2014 salary of $4.675 million should be the ceiling.

Given the above, Cody Allen is likely to receive somewhere between $2.75 million and $4.675 million. Now that we have a range, let’s find someone towards the middle.

In 2013, Ernesto Frieri recorded 37 SV with a 90.2 SV%, 98 SO and a 3.80 ERA. Over his career he recorded 60 saves with an 89.55 SV%, 5.62 WPA, 2.3 fWAR and 2.76 ERA Those numbers are quite similar across the board with both players having an identical career save total and only 3 more platform saves. Frieri’s 2014 salary was $3.80 million so we can determine Allen will receive a similar amount. Andrew Bailey ($3.9 million in 2012) is a decent comp as well.

As for my model, Allen projects to receive $3,595,732 +/- $130,998 which is perfectly in line with the comps above. MLBTradeRumors projects him at $3.5 million so both of our models are very close here (and will be most of the time).

For a player who has already been through the arbitration process before, the valuation is completely different as career statistics are no longer used the 2nd, 3rd, 4th, etc. time around (except in a few rare cases).

For a RP who has previously been through the arbitration process, the stats that correlate most with eventual salary are:

(1) Platform SV: 70.40%

(2) Platform fWAR: 41.36%

(3) Platform RA9-WAR: 36.58%

(4) Platform SV%: 34.79%

(5) Platform WPA: 34.34%

(6) Platform SO: 30.04%

For example, let’s look at Reds closer Aroldis Chapman who is arbitration-eligible for the third time going into 2016. As an Arb-2 going into 2015, Chapman received a $8.05 million salary. That figure includes everything he had done in his career up to that point. Thus, when determining his 2016 salary, we don’t need to focus on previous seasons. We need only determine what his 2015 season was worth and give him a raise. In his platform season (2015), Chapman recorded 33 saves with a 91.67 SV%, 116 SO, 1.99 WPA, 2.4 fWAR, 2.7 RA9-WAR and a 1.63 ERA. We want to find the players whose stats are most similar to Chapman.

First, let’s discuss Juan Carlos Oviedo’s (formally known as Leo Nunez) 2011 platform season where he recorded 36 saves with an 85.70%, 55 SO, 1.07 WPA, 0.1 fWAR, 0.2 RA9-WAR and a 4.06 ERA. Although Oviedo was fortunate enough to record more saves, Chapman was the far better player overall; so much so that, despite having fewer saves, we can determine that Chapman will definitely receive a larger raise than the $2.35 million raise Oviedo received going into 2012. Therefore, we can consider a raise of $2.35 million to be his floor. Oviedo is the perfect example of how important saves are (for arbitration purposes) when it comes to relievers.

Next, let’s look at Heath Bell’s 2010 platform season (again old, but useful still) where he recorded 47 saves with a 94.0 SV%, 86 SO, 4.49 WPA, 2.3 fWAR, 2.6 RA9-WAR and a 1.93 ERA. Like Chapman, Bell was an All-Star closer with virtually identical numbers except for WPA and SV, where Bell clearly outproduced him. Moreover, Bell was named the NL reliever of the year. As such, Bell’s raise of $3.5 million going into 2011 should be the ceiling.

Given the above, Aroldis Chapman is likely to receive a raise somewhere between $2.35 million and $3.5 million for a final salary between $10.4 million and $11.55 million.

Chapman is a perfect example of why first determining a range is important as Chapman represents a type of player who just has not been through the arbitration process in this service group before. Since 2006, there has not been a closer who recorded less than 40 saves with dominant numbers. Looking at saves we have Chris Perez (39 saves – $2.8 million in 2013), Brandon League (37 saves – $2.75 million in 2012), Jonathan Papelbon (37 saves – $2.65 million in 2011) and Joel Hanrahan (36 saves -$2.94 million in 2013). Somewhere around those numbers and perhaps a bit higher is what we should expect.

My model projects that Chapman should receive a raise of $2,743,587+/- $152,366 for a total 2016 salary of $10,793,587+/- $152,366, although I think my projection underestimates the impact his dominant numbers will have despite the lowish save totals (due the lack of comps). I would expect a raise of around $3 million. MlbTradeRumors is projecting a raise of $4,850,000 for a total salary of $12,900,000, which not only surpasses Heath Bell’s raise, but shatters Jim Johnson’s record-setting raise for a non-first-year reliever of $3,875,000 when he recorded 51 of 54 saves in 2012. Given the importance of saves and the relative unimportance of the other stats, I don’t see how such a high number is possible. Nonetheless, Chapman is a very interesting case study as he has the potential to change the way relievers are viewed during the arbitration process.

Next up: position players.

Brandon Crawford is coming off an All-Star season in which he not only won his first Gold Glove, but his first Silver Slugger as well. The last to win both awards in San Francisco? Barry Bonds in 1997. Although Crawford may not have all the tools that Bonds did, he has come a long way since he made his debut at shortstop for the Giants in May of 2011. Crawford entered the league projected as a shortstop with plus defense, but also as an offensive project. So what sparked him to have the second-most home runs (21) among all shortstops, more than his totals from 2013 and 2014 combined, and a SLG% of .462 that led the all other qualified shortstops in the league by more than 20 points? An aggressive approach at the plate paired with slight mechanical adjustments. Consider Crawford’s Z-Swing%:

Now consider his hard-hit%:

These graphs, courtesy of data from FanGraphs, tell an interesting story. For the first four years of his career, Crawford’s Z-Swing% and hard-hit% had a direct correlation. In the first two years of his career, Crawford had a Z-Swing% that was barely above average in the league and a hard-hit% that was below average. Last year, however, his Z-Swing% skyrocketed to more than 8% above league average and he had a hard-hit% that was, for the first time in his career, above average. Yet, there is something odd about his recent success at the plate.

Crawford was not making more contact than in the past; he had just improved on the quality of contact with his new swing and more aggressive approach. Last year, he posted the 16th-worst SwStr% (percentage of swings and misses) in the league at 13.6% and a below-average 73.6% Contact%. Crawford also showed more aggression on pitches outside the zone, posting an O-Swing% (percentage of swings on pitches outside the zone) of 35.2% which is also worse than the league average of 31.8%. All of these were the worst numbers in their respective categories for his young career.

Despite all of this, his aggression at the plate and his change in mechanics led him to become a top power hitter at his position last year and a legitimate threat in the second half of the Giants batting order. Although the trend in these numbers may be hard to fully validate due to the small sample size, the new-found pop in the bat could make Crawford a much more valuable player (as finding power among shortstops in today’s league is a rarity). If his Z-Swing% and hard-hit% continue to be linearly related, Crawford may very well continue his progress in 2016 and bring power to a Giants lineup that was fourth to last in total home runs last season. One thing is for certain: his flow will remain among the game’s elite.

(pearlswithplaid, pearlswithplaid.blogspot.com)

This article was co-authord by Daanish Mulla (@DanMMulla)

A New York Times article by John Branch in October 2015 discussed the elusive definition of the pitching term “stuff”. Talk of “plus stuff” and feelings of “all the stuff being there” was scattered throughout the article. Despite interesting commentary discussing the ability for pitchers to over-power hitters, there was no true definition of the nastiness of a pitcher’s stuff.

Earlier this November, Eno Sarris wrote an article examining who had the best changeup in the 2015 season. This was evaluated by looking at the difference in speed and movement with respect to the pitcher’s fastball. This made us think, to truly quantify “stuff”, you would first need to understand what goes into a pitcher having a truly dominant repertoire.

Our definition of a pitcher’s “stuff”, or their overall nastiness, was based on three different factors: 1) fastball velocity; 2) change of velocity of a secondary pitch with respect to the fastball; and 3) movement with respect to the fastball. We downloaded all of FanGraphs’ PITCHf/x data from 2008 to 2015 to attempt solving this problem.

For a pitch to qualify for this analysis, it had to be thrown by an individual pitcher at a frequency equal to, or greater than, the average frequency for that pitch to be thrown throughout the entire data set. For example, in our data set, the curveball was thrown at an average of 12% of the time by all pitchers. Thus, a pitcher’s curveball was only considered if it was thrown at a frequency of greater than or equal to 12%. We then determined the maximum and minimum velocity for all eligible pitches for each pitcher. Working off of the fastball, we then determined the maximum change in movement in both the X direction, and the Z direction, for any qualifying pitches. We then calculated the maximum resultant movement for these values. Z-scores were then calculated and summed from the following factors to get a final pitcher “stuff” score: 1) maximum velocity; 2) change in velocity between maximum and minimum velocity; and 3) maximum resultant movement.

Here is an example as to how a pitcher with elite stuff performed in this analysis. David Price had a great year with the Blue Jays and Tigers. From FanGraphs data, his maximum pitch velocity was 94.1 mph, and the minimum pitch velocity was 85.2 mph – a difference of 8.9 mph. Working off the fastball, the greatest x direction break on a pitch was 15.1”, and the greatest z direction break was 10.9”.  This produced a resultant change in movement of 18.6”.

These values translated to a z scores for velocity, change in velocity, and resultant movement of 0.969, -0.08, 0.91, resulting in a stuff value of 1.80. Comparatively, another Blue Jays starter who struggled in 2015 was Drew Hutchinson. Hutchison had a fastball velocity of 92.4 mph, an offspeed pitch of 84.3 mph, an x direction break of 7.1, and a z direction break of 9.8. Corresponding z scores for velocity, change in velocity, and resultant break were 0.392, -0.24, -0.08, resulting in a stuff value of 0.1.

To break down how well our stuff rating was performing, we correlated stuff with K/9. Pitchers included in this analysis were all starting pitchers who pitched 90 innings in a season, between the 2008 and 2015 season. Average stuff and average K/9 was calculated during this time. Overall, the correlation was r = 0.42 (Figure 1). For the sake of these graphs, knuckleballers Tim Wakefield and R.A. Dickey were not included, as the stuff metric had them rated lower than -4 per season.

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Figure 1. Stuff vs K/9, between the 2008 and 2015 MLB season.

Here’s the top 25 starting pitchers from the 2015 season ranked by their stuff. While overall, we think this is a good starting point for evaluating a pitcher’s repertoire, there are a few notable pitchers that the stuff calculation doesn’t seem to do justice. Chris Archer, who has had his slider called one of the best pitches in all of baseball, has only a 1.12 stuff value, and is ranked as having the 67^th best stuff. Max Scherzer, who threw two no-hitters, is ranked as only having the 60^th best stuff.

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Table 1. Top 25 stuff for pitchers, with raw data on velocity and break

What’s worth stressing however, is that this metric serves to evaluate the individual pitches within their repertoire. There are pitchers which would be scouted to have the ability to throw hard, with lots of break. Pitching is clearly an art form that involves more than those two things, thus players like Mark Buerhle (-2.7), are clearly someone who has mastered the art of pitching, without having great stuff.  When comparing stuff against xFIP, correlation coefficients are smaller (r = -0.33) (Figure 2). Much like K/9 does not directly predict pitcher success, neither does stuff.

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Figure 2. Stuff vs. xFIP, between the 2008 and 2015 season.

We believe there’s great use for this metric. We think this metric can provide insight into how stuff changes with age, how stuff changes after a pitcher is injured, and how it can let a coach know when a player has returned to pre-injury form, and how a pitcher’s consistency with their stuff relates to success. As with any ranking that appears on the FanGraphs website, we’re sure that there will be debate – however, we are looking forward to the input from the community into how we can improve this technique.

References

Branch, J. (2015). The Mysteries of Pitching, and All That ‘Stuff’. Posted online, October 3, 2015. http://www.nytimes.com/2015/10/04/sports/baseball/the-mysteries-of-pitching-and-all-that-stuff.html

Sarris, E. (2015). The Best Changeups of the Year by Shape and Speed. Posted online, November 9, 2015. http://www.fangraphs.com/blogs/the-best-changeups-of-the-year-by-shape-and-speed/